# Are Updatable Learned Indexes Ready?

arXiv: https://arxiv.org/abs/2207.02900 · HTML: https://mlsanity.com/paper/2207.02900/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2022.

## Built on by (most cited first, 5 of 5)

- [Theoretical Analysis of Learned Database Operations under Distribution Shift through Distribution Learnability](https://mlsanity.com/paper/2411.06241/) (2024)
- [LeCo: Lightweight Compression via Learning Serial Correlations](https://mlsanity.com/paper/2306.15374/) (2023)
- [Learned Compression of Nonlinear Time Series With Random Access](https://mlsanity.com/paper/2412.16266/) (2024)
- [DobLIX: A Dual-Objective Learned Index for Log-Structured Merge Trees](https://mlsanity.com/paper/2502.05369/) (2025)
- [Piecewise Linear Approximation in Learned Index Structures: Theoretical and Empirical Analysis](https://mlsanity.com/paper/2506.20139/) (2025)

## Abstract (arXiv)

Recently, numerous promising results have shown that updatable learned indexes can perform better than traditional indexes with much lower memory space consumption. But it is unknown how these learned indexes compare against each other and against the traditional ones under realistic workloads with changing data distributions and concurrency levels. This makes practitioners still wary about how these new indexes would actually behave in practice. To fill this gap, this paper conducts the first comprehensive evaluation on updatable learned indexes. Our evaluation uses ten real datasets and various workloads to challenge learned indexes in three aspects: performance, memory space efficiency and robustness. Based on the results, we give a series of takeaways that can guide the future development and deployment of learned indexes.